Numerous offline filter pruning techniques to rank and prune filters have been proposed. These methods can be categorized into two groups: those that analyze the filters themselves and those that perform pruning based on filter activations. Ranking and pruning based on filter analysis is performed using various measures including L1 norm [1]}, L1-norm and standard deviation [2]}, Entropy [3]}, Geometric Median [4]} and average percentage of zero activations [5]}. A new optimization method that enforces correlation among filters and then safely removes the redundant filters has also been proposed [6]}. Filter activation maps inform us about whether the certain filter fires on a given dataset. Thus a set of filters which may have a very distinctive shape may result in a very low activation and thus can be pruned for a particular dataset. Similarly, similar filters will result in similar activations and thus redundancy can also be identified via activation maps.
